Arreglado formato de salida de video para Gradio

app.py

@@ -397,7 +397,7 @@ def load_image_model(model_name):
                 
                 # Habilitar optimizaciones de memoria (más conservadoras)
                 if hasattr(pipe, 'enable_attention_slicing'):
-                    pipe.enable_attention_slicing()
+            pipe.enable_attention_slicing()
                     print("✅ Attention slicing habilitado")
                 
                 # Deshabilitar CPU offload temporalmente (causa problemas con ZeroGPU)
@@ -418,7 +418,7 @@ def load_image_model(model_name):
                         try:
                             pipe.enable_xformers_memory_efficient_attention()
                             print("✅ XFormers memory efficient attention habilitado")
-                        except Exception as e:
+                except Exception as e:
                             print(f"⚠️ XFormers no disponible: {e}")
                             print("🔄 Usando atención estándar")
             
@@ -438,7 +438,7 @@ def load_image_model(model_name):
             
             # Guardar en cache
             model_cache[model_name] = pipe
-            
+                
         except Exception as e:
             print(f"❌ Error cargando modelo {model_name}: {e}")
             print(f"🔍 Tipo de error: {type(e).__name__}")
@@ -446,8 +446,8 @@ def load_image_model(model_name):
             # Intentar cargar sin variant fp16 si falló
             if "variant" in str(e) and "fp16" in str(e):
                 print("🔄 Reintentando sin variant fp16...")
-                try:
-                    pipe = StableDiffusionPipeline.from_pretrained(
+            try:
+                pipe = StableDiffusionPipeline.from_pretrained(
                         model_name, 
                         torch_dtype=torch_dtype,
                         use_auth_token=HF_TOKEN if HF_TOKEN and ("flux" in model_name.lower() or "black-forest" in model_name.lower()) else None
@@ -777,7 +777,7 @@ def generate_image(
                             
                             result = pipe(**generation_kwargs)
                             image = result.images[0]
-                        else:
+            else:
                             print("✅ Imagen generada correctamente")
                     else:
                         print("❌ Error: Imagen vacía")
@@ -931,7 +931,7 @@ def generate_video(prompt, model_name, num_frames=16, num_inference_steps=20):
                     print(f"Forma no reconocida: {video_frames.shape}")
                     return None
             else:
-                return video_frames
+        return video_frames
         
     except Exception as e:
         print(f"Error generando video: {str(e)}")
@@ -1369,7 +1369,7 @@ with gr.Blocks(title="Modelos Libres de IA", theme=gr.themes.Soft()) as demo:
                 update_model_info,
                 inputs=[image_model],
                 outputs=[model_info]
-            )
+                    )
             
             image_btn.click(
                 generate_image,
@@ -1434,17 +1434,17 @@ with gr.Blocks(title="Modelos Libres de IA", theme=gr.themes.Soft()) as demo:
             )
 
         # Agregar endpoint para generate_video_with_info dentro del contexto de Gradio
-        demo.load(
-            generate_video_with_info,
-            inputs=[
-                gr.Textbox(label="Prompt", placeholder="Describe el video..."),
-                gr.Dropdown(choices=list(MODELS["video"].keys()), label="Modelo"),
-                gr.Dropdown(choices=["speed", "balanced", "quality"], value="balanced", label="Optimización"),
-                gr.Image(label="Imagen de entrada (opcional)", type="pil")
-            ],
-            outputs=gr.Video(label="Video Generado", format="mp4"),
-            api_name="generate_video_with_info"
-        )
+demo.load(
+    generate_video_with_info,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Describe el video..."),
+        gr.Dropdown(choices=list(MODELS["video"].keys()), label="Modelo"),
+        gr.Dropdown(choices=["speed", "balanced", "quality"], value="balanced", label="Optimización"),
+        gr.Image(label="Imagen de entrada (opcional)", type="pil")
+    ],
+    outputs=gr.Video(label="Video Generado", format="mp4"),
+    api_name="generate_video_with_info"
+)
 
 # Configuración para Hugging Face Spaces
 if __name__ == "__main__":